Immigrant and Racialized Populations’ Cumulative Exposure to Discrimination and Associations with Long-Term Conditions During COVID-19: A Nationwide Large-Scale Study in Canada
Bibliographic record
Abstract
BACKGROUND: This cross-sectional study examines associations between the race-migration nexus, cumulative exposure to intersectional discrimination (2 years before and during the COVID-19 pandemic), and long-term conditions. METHODS: A nationwide self-selected sample (n = 32,605) was obtained from a Statistics Canada's Crowdsourcing online survey from August 4 to 24, 2020. Binary and multinomial logistic regression models were used to examine disparities by the race-migration nexus in accumulative experiences of multiple situations- and identity-based discrimination and their relations with long-term conditions, after controlling for sociodemographic covariates. RESULTS: During the pandemic, discrimination stemming from racialization - such as race/skin color (24.4% vs 20.1%) and ethnicity/culture (18.5% vs 16.5%) - and cyberspace (34.1% vs 29.8%) exaggerated relative to pre-pandemic period; compared to Canadian-born (CB) whites, the likelihood of experiencing multiple discrimination increased alongside the domains of discrimination being additively intersected (e.g., identity-based, all p's < 0.001) among CB racialized minorities (ORs 2.08 to 11.78), foreign-born (FB) racialized minorities (ORs 1.99 to 12.72), and Indigenous populations (ORs 1.62 to 8.17), except for FB whites (p > 0.01); dose-response relationships were found between cumulative exposure to multiple discrimination and odds of reporting long-term conditions (p's < 0.001), including seeing (ORs 1.63 to 2.99), hearing (ORs 1.83 to 4.45), physical (ORs 1.66 to 3.87), cognitive (ORs 1.81 to 3.79), and mental health-related impairments (ORs 1.82 to 3.41). CONCLUSIONS: Despite a universal health system, Canadians who are CB/FB racialized and Indigenous populations, have a higher prevalence of cumulative exposure to different aspects of discrimination that are associated with multiple long-term conditions during the COVID-19 pandemic. Equity-driven solutions are needed to tackle upstream determinants of health inequalities through uprooting intersectional discrimination faced by racialized and immigrant communities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".